Detectron2 Checkpoint Not Found: A Guide to Troubleshooting Your Deep Learning Models
The Problem: You're excited to use Detectron2, a powerful object detection library, but when you try to load a pre-trained model, you encounter the dreaded "Checkpoint not found" error. This often happens when the model weights or configuration files are missing or the paths are incorrect.
Imagine this: You've downloaded a pre-trained Detectron2 model, ready to apply it to your image dataset. You've followed the instructions, but when you try to load the model, you get a message stating that the checkpoint cannot be found. Frustrating, right?
Let's dive into the common culprits and solutions:
Scenario:
from detectron2.config import get_cfg
from detectron2.engine import DefaultPredictor
# Load the pre-trained model
cfg = get_cfg()
cfg.MODEL.WEIGHTS = "path/to/model/weights.pth" # Replace with your model path
predictor = DefaultPredictor(cfg)
The above code attempts to load a pre-trained model. If you get the "Checkpoint not found" error, here's what you need to do:
-
Verify the Path: Double-check that the
MODEL.WEIGHTSpath in your configuration file is correct and points to the actual location of the model weights file (usually.pth). Typos or incorrect capitalization can cause problems. -
Download Missing Files: If you're using a pre-trained model from Detectron2's model zoo, ensure you have downloaded all necessary files. This typically includes the configuration file (
.yaml) and the model weights file (.pth). Download them from the official repository or the provided link. -
Check Model Zoo: Ensure you're using the correct model name. Detectron2's model zoo provides different models for different tasks. Make sure you're using the right model configuration file and weights for your desired task.
-
Install Required Libraries: Detectron2 relies on several external libraries. Ensure you have all required dependencies installed. Run
pip install detectron2 -Uto update to the latest version, and then install any additional libraries mentioned in the model documentation. -
Compatibility: If you're using an older Detectron2 version or a model trained with a different version, compatibility issues might arise. Ensure you're using a compatible version of Detectron2 and model weights.
-
Check Configuration File: The configuration file (
.yaml) contains settings related to the model, including the path to the weights file. Double-check that theMODEL.WEIGHTSparameter is set correctly in the configuration file.
Additional Tips:
- Print the Path: Insert a
print(cfg.MODEL.WEIGHTS)statement before loading the model to verify the path being used by Detectron2. - Absolute Path: Use absolute paths instead of relative paths to avoid confusion and ensure the model is loaded correctly.
- Environment Variables: Consider setting environment variables to store common paths, making them easily accessible across your project.
Remember: Always refer to the official Detectron2 documentation and model-specific instructions for detailed information and specific solutions for your particular setup.
By following these steps, you should be able to successfully load your pre-trained Detectron2 model and start using it for your object detection tasks.
Resources:
- Detectron2 Documentation: https://detectron2.readthedocs.io/
- Detectron2 Model Zoo: https://github.com/facebookresearch/detectron2/blob/main/MODEL_ZOO.md
- Detectron2 Github Repository: https://github.com/facebookresearch/detectron2
